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Model building will be repeated using a backward method to check the final model.
Potential confounders who were not significantly related (P-values > 0.05) to a subsequent diagnosis of depression were excluded from the final analyses using a backward method.
This multivariate model was reduced by using a backward method with a probability of staying in the model of P<0.05.
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The significant factors in the response equations were maintained, whereas the insignificant ones were automatically eliminated using a backward reduction method.
Non-significant predictors were eliminated using a backward elimination method.
Using a backward elimination method multivariable logistic regression were developed (Table 3).
Multiple logistic regression analysis was performed using a backward selection method.
Univariate associations with a p-value < 0.25 were used to build a multivariate model using a backward elimination method.
Further peer-reviewed references as well as websites are added using a backward snowball method (pursuing references of references) [ 17].
The final set of covariates was determined using a backward selection method based on significance in regression analysis.
Specifically, the method of lines is used to reduce the governing PDEs to a system of ODEs, which is then solved using a backward differentiation method.
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